How to apply function to multiple columns based on value of another column?

Question

Let's say I have a dataframe as follow:

unit	altitude_low	altitude_high
meter	456	25435
meter	254	35223
feet	34	3256
feet	46	234
meter	456	45776

How do I convert the value of both altitude columns to meter if the unit column is feet? I tried:

def convert_to_m(row):  
    if row['unit'] == "feet":
        row['altitude_low ']= row['altitude_low ']/3.281
        row['altitude_high']= row['altitude_high']/3.281 
    else:
        pass 

df= df.apply(lambda row: convert_to_m(row), axis= "columns")

but this failed.

mozway · Accepted Answer · 2022-03-17 02:51:10Z

1

You can use boolean indexing:

df.loc[df['unit'].eq('feet'), ['altitude_low','altitude_high']] /= 3.281

Output:

    unit  altitude_low  altitude_high
0  meter    456.000000   25435.000000
1  meter    254.000000   35223.000000
2   feet     10.362694     992.380372
3   feet     14.020116      71.319720
4  meter    456.000000   45776.000000

You can take the opportunity to change the unit as well:

mask = df['unit'].eq('feet')
df.loc[mask, ['altitude_low','altitude_high']] /= 3.281
df.loc[mask, 'unit'] = 'meter'

Output:

    unit  altitude_low  altitude_high
0  meter    456.000000   25435.000000
1  meter    254.000000   35223.000000
2  meter     10.362694     992.380372
3  meter     14.020116      71.319720
4  meter    456.000000   45776.000000

alternative approach for multiple units:

Use a dictionary with the conversion factors

units = {'feet': 1/3.281, 'meter': 1}

cols = ['altitude_low','altitude_high']
df[cols] = df[cols].mul(df['unit'].map(units), axis=0)

edited Mar 17, 2022 at 2:51

answered Mar 17, 2022 at 2:38

mozway

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2 Comments

newbiedataanalyst Over a year ago

holy moly thanks a lot dude! you solve my problem!

mozway Over a year ago

@anjelitodevan if you have multiple units, you can use a dictionary, see update ;)

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